Antibacterial activity of selected medicinal plants of northwest Pakistan traditionally used against mastitis in livestock

The present study aimed to investigate the efficacy of traditionally used anti-mastitis plants (Allium sativum, Bunium persicum, Oryza sativa and Triticum aestivum) in northwest Pakistan against bacterial pathogens. Selected plants were phytochemically screened for Alkaloids, Flavonoids, and Saponins and checked for in vitro antibacterial activity at concentration of 50 mg/ml against S. aureus, E. coli and K. pneumoniae by agar well diffusion method. Minimum inhibitory concentration and minimum bactericidal concentration was determined against multidrug resistant bacteria using tube dilution method. All extracts were found to significantly inhibit (p < 0.01, p < 0.05) the activity against bacterial strains examined. Among phytochemicals, alkaloids of all tested antimastitis plants produced significantly higher inhibition zones against bacteria. The minimum inhibitory concentration and minimum bactericidal concentration of phytochemicals and crude methanolic extracts against tested bacterial strains ranged between 12.5–50 mg/ml and 25–50 mg/ml, respectively. Medicinal plants traditionally used against mastitis are therapeutically active against bacterial pathogens. A. sativum and B. persicum were found to be potential candidate species for the development of novel veterinary drugs with low cost and fewer side effects.     

Prey size diversity hinders biomass trophic transfer and predator size diversity promotes it in planktonic communities

Body size exerts multiple effects on plankton food-web interactions. However, the influence of size structure on trophic transfer remains poorly quantified in the field. Here, we examine how the size diversity of prey (nano-microplankton) and predators (mesozooplankton) influence trophic transfer efficiency (using biomass ratio as a proxy) in natural marine ecosystems. Our results support previous studies on single trophic levels: transfer efficiency decreases with increasing prey size diversity and is enhanced with greater predator size diversity. We further show that communities with low nano-microplankton size diversity and high mesozooplankton size diversity tend to occur in warmer environments with low nutrient concentrations, thus promoting trophic transfer to higher trophic levels in those conditions. Moreover, we reveal an interactive effect of predator and prey size diversities: the positive effect of predator size diversity becomes influential when prey size diversity is high. Mechanistically, the negative effect of prey size diversity on trophic transfer may be explained by unicellular size-based metabolic constraints as well as trade-offs between growth and predation avoidance with size, whereas increasing predator size diversity may enhance diet niche partitioning and thus promote trophic transfer. These findings provide insights into size-based theories of ecosystem functioning, with implications for ecosystem predictive models.     

Comparing the Clinical Outcomes between Drug Eluting Stents and Bare Metal Stents in Patients with Insulin-Treated Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis of 10 Randomized Controlled Trials

BackgroundSeveral studies have shown Drug Eluting Stents (DES) to be better compared to Bare Metal Stents (BMS) in patients with type 2 Diabetes Mellitus (T2DM). Since, the adverse clinical outcomes in patients with Insulin-Treated Type 2 Diabetes Mellitus (ITDM) implanted with DES and BMS have not been previously studied, we aim to compare the clinical outcomes in similar patients with cardiovascular diseases, treated with DES and BMS.MethodsRandomized Controlled Trials (RCTs) comparing patients treated with DES and BMS were searched from PubMed and EMBASE databases. Outcome data for the patients with ITDM were carefully extracted. Major Adverse Cardiac Events (MACEs), mortality, Target Vessel Revascularization (TVR), Target Lesion Revascularization (TLR), Myocardial Infarction (MI) and Stent Thrombosis (ST) were considered as the clinical endpoints for this analysis. Odds ratios (OR) with 95% confidence intervals (CIs) were calculated and the pooled analyses were performed with RevMan 5.3 software.ResultsTen RCTs consisting of 830 patients with ITDM (477 patients in the DES group and 353 patients in the BMS group) from a total number of 9,141 patients were included in this analysis. During a follow-up period from one month to one year, MACEs were not increased with the use of DES in these patients with ITDM. At 9 months, MACEs were significantly lower in the DES group with OR: 0.40, 95% CI: 0.23–0.72; P = 0.002 with no increase in mortality. TVR and TLR also favored the DES group with OR: 0.44, 95% CI: 0.22–0.88, P = 0.02 and OR: 0.28, 95% CI: 0.14–0.53; P = 0.0001 respectively at 9 months, and OR: 0.46, 95% CI: 0.23–0.94, P = 0.03 and OR: 0.28, 95% CI: 0.14–0.55; P = 0.0003 respectively at one year. Results for MI, and ST were not statistically significant.ConclusionCompared to BMS, DES were associated with a significantly lower rate of repeated revascularization, without any increase in MACEs or mortality in these patients with ITDM during a follow up period of one year. However, due to the very small population size, further studies with a larger number of randomized patients are required to completely solve this issue.     

Taxifolin prevents postprandial hyperglycemia by regulating the activity of α-amylase: Evidence from an in vivo and in silico studies

There has been a dramatic increase in the prevalence of diabetes mellitus (DM) and its associated complications globally. The postprandial stage of DM involves prompt elevation in the levels of blood glucose and α-amylase, a carbohydrate-metabolizing enzyme is mainly involved in the regulation of postprandial hyperglycemia. This study was designed to assess the ability of a well-known flavonoid, taxifolin (TFN), against postprandial hyperglycemia and its inhibitory effects on α-amylase activity through the assessment of therapeutic potentials of TFN in an alloxan-induced diabetic animal model. The binding potential TFN with an α-amylase receptor was also investigated through molecular dynamics (MD) simulation and docking of to compare the binding affinities and energies of TFN and standard drug acarbose (ACB) with target enzyme. TFN significantly improved the postprandial hyperglycemia, lipid profile, and serum levels of α-amylase, lipase, and C-reactive protein in a dose-dependent manner when compared with that of either DM-induced and ACB-treated alloxan-induced diabetic rats. Moreover, TFN also enhanced the anti-oxidant status and normal functioning of the liver in alloxan-induced diabetic rats more efficiently as compared to that of ACB-treated alloxan-induced diabetic rats. Therapeutic potentials of TFN were also verified by MD simulation and docking results, which exhibited that the binding energy and affinity of TFN to bind with receptor was significantly higher as compared to that of ACB. Hence, the results of this study signify that TFN might be a potent inhibitor of α-amylase that has the potential to regulate the postprandial hyperglycemia along with its anti-inflammatory and anti-oxidant properties during the treatment of DM.     

CompareSVM: supervised,Support Vector Machine (SVM) inference of gene regularity networks

Background

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.

Results

We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.

Conclusions

For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users.